slmgr /ipk your_license_keyReplace your_license_key with following volumn license keys according to Windows Edition:
| You are Lyra, a master-level AI prompt optimization specialist. Your mission: transform any user input into | |
| precision-crafted prompts that unlock AI's full potential across all platforms. | |
| ## THE 4-D METHODOLOGY | |
| ### 1. DECONSTRUCT | |
| - Extract core intent, key entities, and context | |
| - Identify output requirements and constraints | |
| - Map what's provided vs. what's missing |
| (def users [{:id 1 | |
| :email "michael.lawson@reqres.in" | |
| :first_name "Michael" | |
| :last_name "Lawson" | |
| :avatar "https://reqres.in/img/faces/1-image.jpg"} | |
| {:id 2 | |
| :email "lindsay.ferguson@reqres.in" | |
| :first_name "Lindsay" | |
| :last_name "Ferguson" | |
| :avatar "https://reqres.in/img/faces/2-image.jpg"} |
GitHub Copilot Custom Agent Mode
I modified that beast mode to be more robust, and I love it. Refactored an entire older repo I made from years ago just for fun and was able to do perfectly
I call it "Extensive Mode"
"Extensive Mode.chatmode.md"
The tools should be customized per environment and what you have available,
Before we look at some common commands, I just want to note a few keyboard commands that are very helpful:
Up Arrow: Will show your last commandDown Arrow: Will show your next commandTab: Will auto-complete your commandCtrl + L: Will clear the screenОбновлено: Январь 2026 | Актуальные методы, поддерживаемые активы, пошаговая инструкция и рекомендации по безопасности
В 2026 году ситуация с оплатой цифрового контента в Steam остаётся сложной для пользователей из России, Казахстана, Беларуси и других стран СНГ. Многие международные платёжные системы ограничивают транзакции, а банковские карты часто отклоняются даже при наличии средств.
| #!/usr/bin/env python3 | |
| """ | |
| capture_tls_quic.py — Capture TLS ClientHello and QUIC Initial | |
| -t : capture TLS ClientHello (default) | |
| -q : capture QUIC Initial | |
| -a : capture both (TLS + QUIC) | |
| """ | |
| from __future__ import annotations |
This is the "Iris" dataset. Originally published at UCI Machine Learning Repository: Iris Data Set, this small dataset from 1936 is often used for testing out machine learning algorithms and visualizations (for example, Scatter Plot). Each row of the table represents an iris flower, including its species and dimensions of its botanical parts, sepal and petal, in centimeters.
The HTML page provides the basic code required to load the data and display it on the page (as JSON) using D3.js.
For a more up to date code example with React & D3, see (VizHub: Stylized Scatter Plot)[https://vizhub.com/curran/3d631093c2334030a6b27fa979bb4a0d?edit=files&file=index.js].
| ---------------- | |
| -- 报文结构定义 | |
| ---------------- | |
| CMPPv2_Field_Protos = { | |
| Command_Name = { | |
| [0x00000001] = "CMPP_CONNECT", | |
| [0x80000001] = "CMPP_CONNECT_RESP", | |
| [0x00000002] = "CMPP_TERMINATE", | |
| [0x80000002] = "CMPP_TERMINATE_RESP", | |
| [0x00000004] = "CMPP_SUBMIT", |